AA-ICP: Iterative Closest Point with Anderson Acceleration
This is an incremental improvement for mobile platforms and resource-limited applications needing faster scan-matching.
The paper tackles the computational expense of Iterative Closest Point (ICP) for real-time applications by proposing AA-ICP, which uses Anderson acceleration to speed up ICP without major code changes, showing significant acceleration in benchmarks on real-world data.
Iterative Closest Point (ICP) is a widely used method for performing scan-matching and registration. Being simple and robust method, it is still computationally expensive and may be challenging to use in real-time applications with limited resources on mobile platforms. In this paper we propose novel effective method for acceleration of ICP which does not require substantial modifications to the existing code. This method is based on an idea of Anderson acceleration which is an iterative procedure for finding a fixed point of contractive mapping. The latter is often faster than a standard Picard iteration, usually used in ICP implementations. We show that ICP, being a fixed point problem, can be significantly accelerated by this method enhanced by heuristics to improve overall robustness. We implement proposed approach into Point Cloud Library (PCL) and make it available online. Benchmarking on real-world data fully supports our claims.